Raviteja Sista | Computer Science and Artificial Intelligence | Best Researcher Award

Published on by Research Hypothesis

Mr. Raviteja Sista | Computer Science and Artificial Intelligence | Best Researcher Award

Research Scholar at Indian Institute of Technology Kharagpur, India

Raviteja Sista is a dynamic and accomplished researcher specializing in Artificial Intelligence, Deep Learning, and Medical Image Analysis. Currently pursuing his Ph.D. at the Indian Institute of Technology Kharagpur with an outstanding GPA of 9.4, he is a recipient of the prestigious Prime Minister’s Research Fellowship. Raviteja holds an MSc in Signal Processing and Communications from the University of Edinburgh and a Bachelor’s in Electronics and Communication Engineering from Osmania University. His research focuses on developing AI-driven frameworks for surgical planning and outcome prediction, with notable contributions to multimodal graph-based learning and surgical video analysis. He has published extensively in top-tier journals such as Medical Image Analysis and Computers in Biology and Medicine, and has actively contributed to international AI challenges and symposia. His technical expertise, academic excellence, and dedication to solving real-world healthcare problems through AI make him a standout figure in the research community.

Professional Profile 

🎓 Education of Raviteja Sista

Raviteja Sista has pursued a stellar academic path marked by excellence and innovation. He is currently enrolled in a Ph.D. program at the Indian Institute of Technology Kharagpur, specializing in Artificial Intelligence at the Centre of Excellence, where he maintains an impressive GPA of 9.4/10. Prior to this, he earned his Master of Science in Signal Processing and Communications with Distinction from the University of Edinburgh (2019–2020). His foundational engineering training was completed with a Bachelor of Engineering in Electronics and Communication from M.V.S.R. Engineering College, affiliated with Osmania University, where he secured a remarkable 85.34%. Raviteja also boasts an outstanding academic record from his early years, achieving 94.6% in Intermediate studies at Narayana Junior College and a CGPA of 9.8/10 in Class X from Lotus National School, Hyderabad.

💼 Professional Experience of Raviteja Sista

Raviteja Sista has a well-rounded professional background that bridges academia, research, and industry. He is currently a Teaching Assistant at IIT Kharagpur, where he supports academic instruction in AI and deep learning. Over the years, he has held teaching roles at several institutions including SRKR Engineering College, CSI Wesley Institute of Technology, Assam Down Town University, and JNTU Kakinada, demonstrating his commitment to education and knowledge dissemination. Complementing his academic roles, Raviteja also gained valuable industry experience as an Associate Software Developer Intern at Accenture Solutions Pvt. Ltd. and through multiple internships at Defence Research and Development Laboratory (DRDL), Hyderabad. His professional journey reflects a strong blend of research, software development, and teaching expertise, all anchored in the field of artificial intelligence and signal processing.

🔬 Research Interests of Raviteja Sista

Raviteja Sista’s research interests lie at the intersection of artificial intelligence and healthcare, with a strong focus on applying deep learning techniques to complex real-world problems. His core areas of interest include Deep Learning, Medical Image Analysis, Digital Signal Processing, Image Processing, Artificial Intelligence, and Design of Algorithms. He is particularly passionate about developing AI-powered systems for surgical planning and automation, leveraging multimodal data, graph neural networks, and computer vision. His work aims to enhance patient safety, improve clinical outcomes, and drive innovation in intelligent medical systems. Raviteja’s commitment to impactful, interdisciplinary research is evident in his projects and publications, which bridge technical depth with healthcare relevance.

🏅 Awards and Honors of Raviteja Sista

Raviteja Sista has been recognized with several prestigious awards and honors that highlight his academic brilliance and research potential. Most notably, he was awarded the Prime Minister’s Research Fellowship (PMRF) in 2022, one of India’s most esteemed research fellowships supporting exceptional doctoral scholars. He also earned a Certificate of Merit for completing the “Advanced Certification in Artificial Intelligence and Machine Learning” from IIIT Hyderabad in 2019. Additionally, Raviteja demonstrated national-level academic excellence by ranking in the Top 3% among over 1 lakh candidates in GATE 2019, a highly competitive examination for engineering graduates in India. These accolades reflect his consistent pursuit of excellence and his growing reputation as a promising researcher in the field of artificial intelligence.

🧾 Conclusion 

Sista Raviteja stands out as a highly qualified, technically accomplished, and visionary researcher in AI for healthcare. With strong academic credentials, impactful projects, respected publications, and active involvement in the scientific community, he demonstrates clear potential for leadership in scientific research.Despite minor areas of potential growth in independent authorship and translational work, his contributions already meet and, in some cases, exceed the typical benchmarks for the Best Researcher Award.

📚 Publications Top Noted

  1. Title: Deep neural hashing for content-based medical image retrieval: A survey
    Authors: A. Manna, R. Sista, D. Sheet
    Journal: Computers in Biology and Medicine, Volume 196, Article 110547
    Year: 2025
    Citations: –
  2. Title: Artificial Intelligence (AI)–Based Model for Prediction of Adversity Outcome Following Laparoscopic Cholecystectomy—a Preliminary Report
    Authors: R. Agrawal, S. Hossain, H. Bisht, R. Sista, P.P. Chakrabarti, D. Sheet, U. De
    Journal: Indian Journal of Surgery, Volume 87 (1), Pages 52–59
    Year: 2025
    Citations: 1
  3. Title: Exploring the Limits of VLMs: A Dataset for Evaluating Text-to-Video Generation
    Authors: A. Srivastava, R. Sista, P.P. Chakrabarti, D. Sheet
    Conference: Indian Conference on Computer Vision Graphics and Image Processing (ICVGIP)
    Year: 2024
    Citations: –
  4. Title: SimCol3D—3D reconstruction during colonoscopy challenge
    Authors: A. Rau, S. Bano, Y. Jin, P. Azagra, J. Morlana, R. Kader, E. Sanderson, …, R. Sista
    Journal: Medical Image Analysis, Volume 96, Article 103195
    Year: 2024
    Citations: 16
  5. Title: CholecTriplet2022: Show me a tool and tell me the triplet—An endoscopic vision challenge for surgical action triplet detection
    Authors: C.I. Nwoye, T. Yu, S. Sharma, A. Murali, D. Alapatt, A. Vardazaryan, K. Yuan, …, R. Sista
    Journal: Medical Image Analysis, Volume 89, Article 102888
    Year: 2023
    Citations: 29
  6. Title: CholecTriplet2021: A benchmark challenge for surgical action triplet recognition
    Authors: C.I. Nwoye, D. Alapatt, T. Yu, A. Vardazaryan, F. Xia, Z. Zhao, T. Xia, F. Jia, …, R. Sista
    Journal: Medical Image Analysis, Volume 86, Article 102803
    Year: 2023
    Citations: 61
  7. Title: CholecTriplet2022: Show me a tool and tell me the triplet—An endoscopic vision challenge for surgical action triplet detection
    Authors: C.I. Nwoye, T. Yu, S. Sharma, A. Murali, D. Alapatt, A. Vardazaryan, …, R. Sista
    Repository: arXiv, arXiv:2302.06294
    Year: 2023
    Citations: –
  8. Title: I’m GROOT: a multi head multi GRaph netwOrk recognizing surgical actiOn Triplets
    Authors: R. Sista, R. Sathish, R. Agrawal, U. De, P.P. Chakrabarti, D. Sheet
    Conference: ICVGIP 2022
    Year: 2022
    Citations: 1
  9. Title: CholecTriplet2021: A benchmark challenge for surgical action triplet recognition
    Authors: C.I. Nwoye, D. Alapatt, T. Yu, A. Vardazaryan, F. Xia, Z. Zhao, …, R. Sista
    Repository: arXiv, arXiv:2204.04746
    Year: 2022
    Citations: 1
  10. Title: I’m GROOT: a multi head multi GRaph netwOrk recognizing surgical actiOn Triplets
    Authors: S. Raviteja, R. Sathish, R. Agrawal, U. De, P.P. Chakrabarti, D. Sheet
    Conference: ICVGIP
    Year: 2022
    Citations: –
  11. Title: Challenges of Decomposing Tools in Surgical Scenes Through Disentangling The Latent Representations
    Authors: S.L. Gorantla, R. Sista, A. Srivastava, U. De, P.P. Chakrabarti, D. Sheet
    Workshop: ICLR Workshop on Challenges in Applied Deep Learning (ICBNB)
    Year: 2025 (Accepted)
    Citations: –

 

Yue Wu | Machine Learning | Best Researcher Award

Published on by Research Hypothesis

Yue Wu | Machine Learning | Best Researcher Award

Assist. Prof. Dr Yue Wu, Hangzhou Dian, China

Assist. Prof. Dr. Yue Wu is a promising young academician whose work bridges the gap between automation, machine learning, and electronic design automation. Currently serving as an Assistant Professor at the School of Electronics and Information Engineering, Hangzhou Dianzi University, China, he exemplifies research excellence through his interdisciplinary expertise. He earned his Ph.D. from Zhejiang University in Aeronautics and Astronautics and a B.S. from Wuhan University of Technology in Automation. His scholarly interests focus on logic synthesis, physical design, and intelligent prediction algorithms using graph neural networks. Despite his early career stage, Dr. Wu has demonstrated impactful contributions to both academia and industry-relevant applications. His recent publication on pre-routing slack prediction using graph attention networks stands out as a novel solution in the realm of EDA. With a strong academic foundation and active research output, Dr. Wu is a fitting nominee for the Best Researcher Award, representing the next generation of innovation in AI-driven engineering.

Publication Profile

Orcid

Education

Dr. Yue Wu has a solid educational foundation in engineering and automation. He earned his Bachelor of Science (B.S.) in Automation from the Wuhan University of Technology in 2018. There, he developed a robust understanding of control systems, signal processing, and computational modeling. Pursuing his academic passion, he undertook doctoral studies at the School of Aeronautics and Astronautics, Zhejiang University, one of China’s premier research institutions. He completed his Ph.D. in 2023, focusing on interdisciplinary topics combining aeronautical engineering, data science, and intelligent systems. His doctoral work incorporated advanced machine learning techniques and their applications in hardware-aware environments, preparing him to lead novel research at the intersection of automation and electronics. This strong academic background equips him with the theoretical depth and practical experience essential for future-forward research in intelligent systems and electronic design automation.

Experience

Dr. Yue Wu is currently serving as an Assistant Professor at the School of Electronics and Information Engineering, Hangzhou Dianzi University, since 2023. Despite being in the early phase of his academic career, he has demonstrated exceptional scholarly promise through teaching, mentorship, and high-impact research. His role involves designing and delivering advanced courses on machine learning, logic circuits, and digital system design while actively supervising undergraduate and graduate research projects. He collaborates with interdisciplinary teams, focusing on the integration of machine learning techniques into physical design and logic synthesis processes, bridging hardware and AI innovations. Prior to this, he was involved in multiple research projects at Zhejiang University during his Ph.D., contributing to algorithm development and experimental validation of graph-based learning techniques. Dr. Wu’s combined expertise in automation, EDA tools, and machine learning positions him as a rising leader in academic research and technological advancement.

Awards and Honors

As a rising scholar, Dr. Yue Wu has been recognized for his academic achievements and research contributions. While specific institutional or national awards are yet to be recorded in the public domain, his selection as a faculty member at Hangzhou Dianzi University, known for its emphasis on electronic and information technology research, is a testament to his academic caliber. His recent first-author publication in the peer-reviewed journal “Automation” (2025) highlights his research excellence and innovation in the application of graph attention networks to pre-routing slack prediction, a complex problem in VLSI design. Additionally, his collaborative projects during his Ph.D. at Zhejiang University received internal recognition and contributed to multiple research grants. Dr. Wu’s research profile is steadily growing, and he is well on the path toward future accolades at the national and international levels as he continues to publish and lead in cutting-edge interdisciplinary domains.

Research Focus

Dr. Yue Wu’s research focuses on the intersection of machine learning and electronic design automation (EDA). His primary interest lies in developing intelligent systems that enhance the physical design and logic synthesis processes used in integrated circuit (IC) design. By leveraging advanced models like graph neural networks (GNNs) and attention-based architectures, Dr. Wu aims to address critical challenges such as slack prediction, timing analysis, and routing optimization. His expertise also extends to hardware-aware machine learning, wherein algorithmic efficiency is optimized for real-world applications in chip manufacturing. His recent work—“Pre-Routing Slack Prediction Based on Graph Attention Network”—demonstrates his ability to combine theoretical AI models with practical EDA problems. By pushing the boundaries of design automation through AI integration, Dr. Wu contributes to faster, smarter, and more power-efficient chip design—critical for the next generation of computing devices. His vision is to make intelligent design automation a core component of future electronics engineering.

Publication Top Notes

Pre-Routing Slack Prediction Based on Graph Attention Network